Abstract: Images have been playing very important role in human life since beginning of human civilization. Applications of images are found ranging from basic but very effective exchange of ideas and information to some advanced technologies in the industry, society, medical and military field. Image data has become an important field of research with rapid and huge growth of visual information in the number of large-scale and online image repositories. “Fuzzy set theory and fuzzy logic provide powerful tools to represent and process human knowledge in form of fuzzy if-then rules. Many difficulties in image processing arise because the data, tasks, and results are uncertain. This uncertainty, however, is not always due to the randomness but to the inherent ambiguity and vagueness of image data. Beside randomness-which can be managed by probability theory-other kinds of imperfection in image processing include grayness ambiguity, geometrical fuzziness, and vague knowledge of image features. This paper imports fuzzy logic concept into image retrieval to simulate these properties of human’s thoughts. Fuzzy method emphasis on adopting the fuzzy language variables to describe the similarity degree of image features, not the features themselves. In this way, we can simulate the nonlinear property of human’s judgments of the image similarity as well as making use of the fuzzy inference to instruct the weights assignment among various image features.
Keywords: Content Based Image Retrieval; Fuzzy Logic; Color Feature, Fuzzy Color Histogram; Fuzzy Colored Image.